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September 2013 Newsletter
Why is Predictive Analytics Important?

Predictive analytics uses technology to predict the future and influence it. Organizations can use historical performance data to extrapolate and make predictions about the future and take actions that would affect those results.

As Dr. Siegel� in his book titled "Predictive Analytics", The Power to Predict who will Click, Buy, Lie or Die states, Predictive Analytics is unique in that it predicts a predefined behavior at an individual or customer level whether it's a consumer or a company. Organizations can set in place specific conditions, which when met would allow an analyst to identify an individual's behavior such as a customer's willingness to return to a store, or a customer's probability of quitting / attriting, etc. Dr. Siegel explains that each individual person or company can be assigned a predictive score as to the behavior that might be valuable for that organization to predict. This behavior may help drive operations for a business or simply offer insights on future events.



"The following video was prepared by Dr. Eric Siegel and answers questions on the application of Predictive Analytics and some of the benefits that can be derived."


Eric Siegel answers eight questions about predictive analytics
Eric Siegel answers eight questions about Predictive Analytics

Predictive analytics differs from traditional business intelligence initiatives in that it adopts a proactive approach to data. Traditional B.I. initiatives use data to learn about a customer or to identify trends in a business. Predictive analytics identifies how that customer will behave in a future situation and how they may react to the various "touch points" a business has with them. 


Predictive analytics empowers organizations to plan for the future, which can transform an uncertainty into a usable action with high probability.


Predictive Analytics is becoming prevalent in many sectors and industries.  We are seeing applications in Financial Services, Telecommunications, Transportation, Health Care, Retail, Manufacturing, Law enforcement to name a few: 


  1. Predictive models are developed to help prevent churn.  The objective of the models is to identify those customers with a propensity to quit, the factors affecting their behavior and allow for an intervention before they quit.
  2. Upsell and Cross sell models are developed to help grow business from existing customers. 
  3. Predictive models are developed to measure the effectiveness of different marketing campaigns.
  4. HR departments are using Predictive models to identify the best candidates for a given position and the likelihood of an employee leaving.
Predictive Analytics offers a unique opportunity to identify future trends and allows organizations to act upon them. As Dr. Siegel states, data is the "collective experience of an organization" and building machines that can harness such data in order to find patterns that hold true in new situations is important. With a growth in big data and the evolving nature of Business Intelligence, Predictive Analytics can offer valuable insights for organizations.




Predictive Analytics trumps intuition.


Source:     1.                 

                2. Eric Siegel answers eight questions about predictive analytics



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